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Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Vogtle-3 shuts down for valve issue
One of the new Vogtle units in Georgia was shut down unexpectedly on Monday last week for a valve issue that has since been investigated and repaired. According to multiple local news outlets, Georgia Power reported on July 17 that Unit 3 was back in service.
Southern Company spokesperson Jacob Hawkins confirmed that Vogtle-3 went off line at 9:25 p.m. local time on July 8 “due to lowering water levels in the steam generators caused by a valve issue on one of the three main feedwater pumps.”
Jae Min Kim, Gyumin Lee, Seung Jun Lee (UNIST)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 422-430
Nuclear power plants have abnormal operating procedures to prepare abnormal events occurring. An operator should choose and follow the appropriate procedure according to alarms and plant parameters which indicate the plant state. However, with enormous information, it is sometimes hard for the operators to judge the plant state in a short period of time. In the field, the skilled operators are well trained in the entry conditions of the abnormal operating procedures, so that they can quickly select a procedure that is appropriate to the current situation. Nevertheless, this task has a potential risk for less skilled operators to make mistakes of the judgement, which would result in response time delayed. Therefore, this paper suggests nuclear power plants abnormality diagnosis algorithm to support the judgement. This paper covers two of three steps to develop the diagnosis system; setting the training data production environment by analyzing the abnormal operating procedures and comparison between deep learning algorithms using the convolutional and recurrent neural networks. The abnormal operating data were generated from the nuclear power plant simulator. In addition, to reduce the dimensionality of the data, principal component analysis was used as data preprocessing. The algorithm is expected to reduce work load of the operators by providing selection of the proper procedure in a short time with high accuracy.